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EfficientNet Convolutional Neural Network for Automated Cervical Spine Fracture Identification 6

EfficientNet Convolutional Neural Network for Automated Cerv...

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6th International Conference on Computational Intelligence and Networks, CINE 2024

作者： Sidpara, Pragati Agarwal, Anshul Swain, Debabrata Gupta, Rajeev Kumar Bhilare, Amol Upkare, Makarand Pandit Deendayal Energy University Dept. of Computer Science and Engineering Gandhinagar India Vishwakarma Institute of Technology Vishwakarma Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798331516796

The aim of this study is to develop an automatic system that uses deep learning techniques to detect cervical spine fractures from medical images. Using a DICOM (Digital imaging and communications in medicine) images of cervical spine, the system can identify a fracture and specify which vertebrae has a fracture. This process involves first processing provided DICOM images, then applying data augmentation techniques including metadata information, finally designing neural network architecture for feature extraction. We use a method called transfer learning, utilizing a pre-trained model like EfficientNet, which is used for feature extraction, while optimizing network parameters using AdanW optimizer and CosineAnnealingWarmup learning rate scheduler. The results demonstrate the successful training of neural network models to identify fracture and that fractured vertebrae, with commendable performance and accuracy level noted during evaluation on validation data. This study has important implications for medical imaging and healthcare industries, since automated fracture detection can increase diagnostic efficiency and accuracy. Moreover, such a system would provide good support to healthcare professionals, specifically radiologists, to identify cervical spine fracture easily. © 2024 IEEE.

关键词： Digital Imaging and Communications in Medicine (DICOM)

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Optimizing Road Extraction in Remote Sensing Imagery: A Lightweight Dilated Inception Deep Residual U-Net with Channel Attention (LDIDRU-CAN) Model and Pruning Strategies 15

Optimizing Road Extraction in Remote Sensing Imagery: A Ligh...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sharma, Palvi Kumar, Rakesh Gupta, Meenu Obaid, Ahmed J. Chandigarh University Dept. of Computer Science & Engineering Punjab India University of Kufa Faculty of Computer Science & Mathematics Najaf Iraq National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350370249

The major challenge in Road Extraction is to extract accurate and complete road network and to get high accuracy while minimizing computational power and ensuring faster prediction. It plays important role in urban planning, disaster management, automatic vehicle navigation and GPS navigation. Several earlier studies used complex deep networks that required large amounts of computational power and large storage. In order to address issues such as high storage requirements, computational load, access to memory delays, and low hardware deployment efficiency this research work presents a novel network model called LDIDRU-CAN. The proposed model is fast, compact, and efficient and requires less storage. The objective of this work, is to compress the original model using structured pruning strategy. This strategy simplified the original model and reduced their computation cost as well as storage requirements. For the reduction of the parameters, we focused on the Row Scale Regular Pruning (RSRP) and Low Learning Rate (LLR) retraining. After applying RSRP pruning approach it reduces storage requirements, parameters, and also contributes to a more than 50% reduction in hardware resource consumption and successfully enhances the parallelism of the model. Testing and validations are performed on the Massachusetts Road Dataset. With only 45 M parameters and 180 MB model size, the suggested network achieves remarkable performance with 87.91% precision, 85.51% recall, 86.69% F1-score and 98.91% accuracy. © 2024 IEEE.

关键词： Channel Attention LLR Pruning Road Extraction RSI RSRP

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Taskgraph Framework: A Competitive Alternative to the OpenMP Thread Model 3

Taskgraph Framework: A Competitive Alternative to the OpenMP...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Chavan, Snehal Nile, Prathamesh Kumar, Sunil Bhowmik, Biswajit National Institute of Technology Karnataka Maharshi Sushrut CAS Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Maharshi Patanjali CPS Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331523893

OpenMP is the predominant standard for shared memory systems in high-performance computing (HPC), offering a tasking paradigm for parallelism. However, existing OpenMP implementations, like GCC and LLVM, face computational limitations that hinder performance, especially for large-scale tasks. This paper presents the Taskgraph framework, a novel solution that overcomes the limitations of traditional task dependency graphs (TDGs). Unlike conventional TDGs, which require costly reconstruction for dynamic program structures, the Taskgraph framework uses a taskgraph clause with a list of variables, enabling real-time adaptation without complete reconstruction. This approach significantly reduces overhead, making the Task-graph model highly efficient for tasks with minimal dependencies, offering a competitive alternative to the OpenMP thread model, and enhancing efficiency and adaptability in dynamic HPC environments. © 2025 IEEE.

关键词： Parallel programming

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Performance Evaluation of Ensemble Learning Techniques for Prediction

Performance Evaluation of Ensemble Learning Techniques for P...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Ramya, S. Srinath, S. Tuppad, Pushpa Jssstu Dept. Computer Science & Engineering Mysore India Jssstu Dept. Environmental Engineering Mysore India

ISBN: (纸本)9798350376876

The paper employed a range of ensemble learning techniques to forecast Chemical Oxygen Demand (COD) levels in Wastewater Treatment Plants (WWTP). By harnessing ensemble learning, the main objective is to enhance the accuracy and robustness of COD prediction models by capitalizing on the diverse strengths of multiple algorithms. Our experiment encompassed two distinct WWTP datasets, allowing us to comprehensively assess the efficiency and effectiveness of three ensemble methods. Firstly, Bagging operates by creating multiple versions of a predictor and combining their outputs to form an aggregated prediction. Secondly, Boosting algorithms such as AdaBoost, GBM, and XGBoost sequentially constructed powerful learners, focusing on previously misclassified instances to bolster predictive performance. Finally, Stacking methodologies combined predictions from multiple models using a meta-learner. The analysis of the results for various models across the two wastewater treatment plant datasets (WWTPDS1 and WWTPDS2) reveals distinct performance levels. The Bagging model exhibits a reasonable performance with WWTPDS1 (MSE: 0.0016, R2: 0.9351) and a slightly lower performance with WWTPDS2 (MSE: 0.0033, R2: 0.6787). The Boosting model outperforms with lower MSE and higher R2 values for both datasets (WWTPDS1 MSE: 0.0010, R2: 0.9751;WWTPDS2 MSE: 0.0028, R2: 0.7384), indicating its better predictive accuracy and reliability. The Stacking model evaluated in the training phase, it showcases exceptionally low MSE and high R2 values (WWTPDS1 MSE: 0.0004, R2: 0. 9 7 1 2;WWTPDS2 MSE: 0.0009, R2: 0.9213). In the testing phase, the Stacking model maintains strong performance (WWTPDS1 MSE: 0.0013, R2: 0. 9 4 9 4;WWTPDS2 MSE: 0.0028, R2: 0. 7 1 9 1). © 2024 IEEE.

关键词： Wastewater treatment

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EBV: Electronic Bee-Veterinarian for Principled Mining and Forecasting of Honeybee Time Series

EBV: Electronic Bee-Veterinarian for Principled Mining and F...

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2024 SIAM International Conference on Data Mining, SDM 2024

作者： Hossain, Shamima Faloutsos, Christos Baer, Boris Kim, Hyoseung Tsotras, Vassilis J. Dept. of Computer Science and Engineering University of California Riverside United States School of Computer Science Carnegie Mellon University United States Dept. of Entomology University of California Riverside United States Dept. of Electrical and Computer Engineering University of California Riverside United States

ISBN: (纸本)9781611978032

Honeybees are vital for pollination and food production. Among many factors, extreme temperature (e.g., due to climate change) is particularly dangerous for bee health. Anticipating such extremities would allow beekeepers to take early preventive action. Thus, given sensor (temperature) time series data from beehives, how can we find patterns and do forecasting? Forecasting is crucial as it helps spot unexpected behavior and thus issue warnings to the beekeepers. In that case, what are the right models for forecasting? ARIMA, RNNs, or something else? We propose the EBV (Electronic Bee-Veterinarian) method, which has the following desirable properties: (i) principled: it is based on a) diffusion equations from physics and b) control theory for feedback-loop controllers;(ii) effective: it works well on multiple, real-world time sequences, (iii) explainable: it needs only a handful of parameters (e.g., bee strength) that beekeepers can easily understand and trust, and (iv) scalable: it performs linearly in time. We applied our method to multiple real-world time sequences, and found that it yields accurate forecasting (up to 49% improvement in RMSE compared to baselines), and segmentation. Specifically, discontinuities detected by EBV mostly coincide with domain expert's opinions, showcasing our approach's potential and practical feasibility. Moreover, EBV is scalable and fast, taking about 20 minutes on a stock laptop for reconstructing two months of sensor data. Copyright © 2024 by SIAM.

关键词： Forecasting

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Detecting adversarial examples using constraint-based learning

Detecting adversarial examples using constraint-based learni...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Kumar, Vivek Kumar, Kamal Singh, Maheep National Institute of Technology Dept. of Computer Science -and Engineering Uttarakhand Pauri India IGDTUW Department of Information Technology New Delhi India Doon University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350394412

Deep learning models have been applied in various fields and continue producing exceptional results. However, these models are vulnerable to adversarial attacks, which are modified data samples maliciously crafted to cause misclassification by a target model. Fields like Network intrusion detection systems (NIDS) are security-critical;therefore, adversarial attacks pose a high risk in such applications. The attacker not only aims for evasion but also tries to launch an attack once the adversarial example has evaded detection by a NIDS. This paper presents a novel technique for detecting adversarial examples targeted towards a NIDS by learning the dependencies between features known as domain constraints. Our proposed method detects adversarial examples that violate these constraints learned during training. Compared with prevalent detection techniques, our method does not require generating adversarial examples for training, which reduces its computational requirement. We assume a grey-box threat model where the attacker can access the training data. We perform experiments on the CICIDS2017 dataset against five state-of-the-art adversarial attacks: FGSM, BIM, PGD, Deep fool and BIM. The results show that our method can detect up to 100% adversarial attacks, outperforming the contemporary schemes. © 2024 IEEE.

关键词： Adversarial machine learning

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Towards Developing a Transformer-Based Bangla Typing Error Correction Model: A Deep Learning-Based Approach 2

Towards Developing a Transformer-Based Bangla Typing Error C...

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2nd International Conference on Information and Communication Technology for Sustainable Development, ICICT4SD 2023

作者： Salim, Shahidul Islam, Taqiul Zannat, Rowzatul Mia, Naeem Fuad, Mohtasim Murad, Hasan Khulna University of Engineering Technology Dept. of Computer Science and Engineering Khulna9203 Bangladesh Chattogram University of Engineering Technology Dept. of Computer Science and Engineering Chattogram4349 Bangladesh

ISBN: (纸本)9798350358667

Despite being the sixth most widely spoken language globally, the Bangla language faces a lack of effective and efficient automated methods to correct keyboard errors in written text. Such errors can result in communication misunderstandings and misinterpretations, particularly in the digital domain, where written text plays a crucial role in communication and is heavily relied upon. However, no keyboard error model for the Bangla language has been found due to the lack of available datasets. In this research work, we have proposed a probabilistic approach to generate a synthetic Bangla keyboard typing error correction (BKTEC) dataset. By utilizing the novel BKTEC dataset, we have fine-tuned a transformer-based model for Keyboard error correction. After training it to convergence, the proposed model has been tested on a test dataset, and we have found a word level accuracy of 88.56%. © 2023 IEEE.

关键词： Error correction

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Deep Learning Algorithms for Pneumonia: A Comparative Approach to Classification and Segmentation 6

Deep Learning Algorithms for Pneumonia: A Comparative Approa...

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6th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Mishra, Vishnu Kumar Mishra, Megha Tiwari, Rajesh Diwan, Tarun Dhar Soni, Rama Patra, Raj Kumar Malla Reddy Engineering college for Women Hyderabad Jntuh Dept. of Computer Science and Engineering Hyderabad India Shri Shankaracharya Technical Campus Csvtu Department of Computer Science and Engineering Bhilai India Atal Bihari Vajpayee University Dept. of Computer Science and CoE Chhattisgarh Bilaspur India Dr. C. V. Raman University Chhattisgarh Bilaspur India CMR Engineering College Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331505790

The study compares the outcomes of numerous modern CNN architectures, such as ResNet-50, VGG-16, and DenseNet-121, with conventional machine learning classifiers in order to assess each architecture's performance. Recent developments in deep learning have shown that medical image analysis, particularly the diagnosis of pneumonia, can be done with great accuracy. This work investigates different deep learning techniques for lung X-ray-based pneumonia classification. The study emphasizes the use of convolutional neural networks (CNNs) to analyze radiographic images and distinguish between pneumonia-affected lung tissue and normal lung tissue.. This underscores the significant potential of deep learning algorithms to enhance the accuracy and efficiency of pneumonia diagnosis, aiding medical professionals in making informed patient care decisions. The results indicate that deep learning methods surpass traditional machine learning techniques in classifying pneumonia, achieving approximately 9 5 % accuracy across all examined CNN models. These deep learning models are trained on an extensive dataset of annotated chest X-rays, which allows them to recognize intricate patterns and characteristics linked to pneumonia. Considering the substantial influence pneumonia has on the world's health, prompt and correct diagnosis is essential to successful management and treatment. © 2024 IEEE.

关键词： Lung cancer

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A Case on Decentralised Use of Blockchains 3

A Case on Decentralised Use of Blockchains

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Sharma, Apurva Murugan, R. Dev Bhoomi Uttarakhand University Dept of Computer Science and Engineering Uttarakhand Dehradun India Department of Computer Science and It Bangalore India

ISBN: (纸本)9798350399264

In recent decades, the technological landscape has experienced a significant transformation, necessitating the development of more robust and effective digital security measures. This scenario is a prevalent application of blockchain technology. The technology facilitates peer-to-peer resource exchanges without the need for an organised authority to oversee and manage the process, utilising a mesh topology. The system operates through the integration of multiple techniques such as encryption, validation protocols, and user verification. The aforementioned solutions facilitate the establishment of a system that enables facile documentation of role in decision-making, thereby affording an additional stratum of protection. The topic at hand pertains to the protective or disclosing attribute. Cryptocurrencies have significantly altered the perception of corporate operations with respect to candour and decentralised management. Blockchain technology has not only laid the groundwork for the cryptocurrency sector, but also holds the potential to transform various sectors such as healthcare, finance, government aid, and education. Its impact on global economics has been significant. The aim of this study is to investigate the topology of the bitcoin protocol, including its properties such as anonymity, concealment, and transparency. Additionally, this research will examine the various fields in which blockchain technology can be applied to address contemporary issues. © 2023 IEEE.

关键词： Blockchain

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BanglaGPT: A Generative Pretrained Transformer-Based Model for Bangla Language 2

BanglaGPT: A Generative Pretrained Transformer-Based Model f...

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2nd International Conference on Information and Communication Technology for Sustainable Development, ICICT4SD 2023

作者： Salim, Md. Shahidul Murad, Hasan Das, Dola Ahmed, Faisal Khulna University of Engineering Technology Dept. of Computer Science and Engineering Khulna9203 Bangladesh Chattogram University of Engineering Technology Dept. of Computer Science and Engineering Chattogram4349 Bangladesh

ISBN: (纸本)9798350358667

Natural Language Processing (NLP) has entered a new era with the advent of pre-trained language models, paving the way for constructing robust language models. Pretrained transformer-based models such as GPT-2 have become prevalent due to their cutting-edge efficiency. However, these approaches rely heavily on resource-intensive languages, forcing other languages to adopt multilingual frameworks (mGPT). The mGPT model could perform better for low-resource languages such as Bangla because the model has been trained on a diverse dataset spanning multiple languages. Recent studies show that the language-specific GPT model outperforms the multilingual mGPT model. In this research, we have proposed a pretrained monolingual GPT model called BanglaGPT using the objective of causal language modeling (CLM). Due to the lack of available large datasets for NLP tasks in Bangla, we have created a Bangla language model dataset called BanglaCLM using a 26.24 GB Bangla corpus scraped from several public websites. We have used a subword-based tokenization algorithm named Byte-Pair Encoding (BPE) for Bangla and finally trained the Bangla-GPT2 model from scratch using the BanglaCLM dataset. Our pretrained BanglaGPT provides state-of-the-art performance for Bangla text generation with a perplexity score of 2.86 and a loss score of 0.45 on the test set. © 2023 IEEE.

关键词： Large dataset

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